Machine Learned Models for Estimation of Sleep Quality in Free-Living Accelerometer Data

(working title)

Esben Lykke, PhD student

28 januar, 2023

Background

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Methods

Overview Network plot of data preparation steps

network plot

Exclusion Criteria

Features

Basic Features

  • Weekday
  • Time of Day
  • Placement
  • Temperature

ACC derived features1

  • Mean ACC X
  • Mean ACC Y
  • Mean ACC Z
  • Standard Deviation X
  • Standard Deviation Y
  • Standard Deviation Z
  • Max Standard Deviation
  • Inclination

Sensor-Independent Features2

  • Clock Proxy Linear
  • Clock Proxy Cosinus

Human Circadian Clock

Forger, Jewett, and Kronauer (1999): a so-called cubic van der Pol equation

\[\frac{dx_c}{dt}=\frac{\pi}{12}\begin{cases}\mu(x_c-\frac{4x^3}{3})-x\begin{bmatrix}(\frac{24}{0.99669\tau_x})^2+kB\end{bmatrix}\end{cases}\]

This thing is dependent on ambient light and body temperature!

Walch et al. (2019) incorporated this feature using step counts from the Apple Watch

But as demonstrated by Walch et al. (2019), a simple cosine function does the tricks just as well :)

Circadian Proxy Features

Circadian Proxy Features

Performance Metrics
Grouped by Event Prediction
Logistic Regression Neural Network Decision Tree XGboost
In-bed Prediction
F1 Score 94.26% 95.88% 95.33% 95.79%
Accuracy 92.95% 95.10% 94.52% 95.01%
Sensitivity 97.96% 96.40% 94.68% 96.02%
Specificity 85.71% 93.24% 94.29% 93.56%
Sleep Prediction
F1 Score 93.08% 94.35% 93.90% 94.29%
Accuracy 90.87% 92.77% 92.29% 92.71%
Sensitivity 94.19% 92.59% 90.98% 92.20%
Specificity 84.65% 93.09% 94.73% 93.69%

Performance of the models to predict each class seperately, i.e., “sleep” and “in-bed”.

Performance Metrics
Grouped by Event Prediction
Logistic Regression Neural Network Decision Tree XGboost
In-Bed Awake
F1 Score 96.00% 96.38% 96.50% 96.54%
Accuracy 92.38% 93.09% 93.32% 93.39%
Sensitivity 97.56% 97.90% 98.17% 98.16%
Specificity 13.11% 19.66% 19.24% 20.51%
In-Bed Sleep
F1 Score 93.12% 94.33% 93.90% 94.29%
Accuracy 90.91% 92.74% 92.29% 92.71%
Sensitivity 94.25% 92.65% 90.98% 92.22%
Specificity 84.65% 92.90% 94.73% 93.65%

Performance of the models to predict each combined class, i.e., “sleep” + “in-bed”.

Bland-Altman Plots

In-bed classification flow

Sleep classification flow

Forger, D. B., M. E. Jewett, and R. E. Kronauer. 1999. “A Simpler Model of the Human Circadian Pacemaker.” Journal of Biological Rhythms 14 (6): 532–37. https://doi.org/10.1177/074873099129000867.
Hirshkowitz, Max, Kaitlyn Whiton, Steven M Albert, Cathy Alessi, Oliviero Bruni, Lydia DonCarlos, Nancy Hazen, et al. 2015. “National Sleep Foundation’s Sleep Time Duration Recommendations: Methodology and Results Summary.” Sleep Health, 4.
Skotte, Jørgen, Mette Korshøj, Jesper Kristiansen, Christiana Hanisch, and Andreas Holtermann. 2014. “Detection of Physical Activity Types Using Triaxial Accelerometers.” Journal of Physical Activity and Health 11 (1): 76–84. https://doi.org/10.1123/jpah.2011-0347.
Walch, Olivia, Yitong Huang, Daniel Forger, and Cathy Goldstein. 2019. “Sleep Stage Prediction with Raw Acceleration and Photoplethysmography Heart Rate Data Derived from a Consumer Wearable Device.” Sleep 42 (12): zsz180. https://doi.org/10.1093/sleep/zsz180.